Loading and Viewing Data

# load packages
library(geosphere)
library(ggplot2)
library(plotly)
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library(ggthemes)
library(RColorBrewer) 
library(sp)
library(maptools)
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library(maps)
library(data.table)
library(tidyverse)
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library(DT)
library(ggmap)
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library(mapproj)
library(rgdal)
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library(leaflet)
library(ggrepel)

# set working directory
setwd("C:/Users/ydeng.AIR/Desktop/Evelyn/1. Columbia/04. DSPP/Presentation 1 - Crime")
# load data 
base::load("police_incidents_df.rdata")
#base::load("uk_aggregated.rdata")
#base::load("uk_aggregated_anti_social.rdata")

# view data
head(police_incidents,10)
#head(uk_aggregated,1000)              
#head(uk_aggregated_anti_social, 1000)

Analysis of Crime by Crime Type

Data Cleaning and Restructuring

# choose only necessary variables
crime <- police_incidents[c(2,4,5,6,10)]
crime
table(crime$Crime.type)
## 
##        Anti-social behaviour                Bicycle theft 
##                      5507770                       272692 
##                     Burglary    Criminal damage and arson 
##                      1254892                      1702860 
##                        Drugs                  Other crime 
##                       430853                       220388 
##                  Other theft        Possession of weapons 
##                      1531002                        91439 
##                 Public order                      Robbery 
##                       755325                       174401 
##                  Shoplifting        Theft from the person 
##                      1077225                       257796 
##                Vehicle crime Violence and sexual offences 
##                      1193693                      3615919
# create dataset with aggregated crime by month by type
crime_freq_by_month <- crime %>%
  group_by(Month, Crime.type) %>%
  summarise(frequency = n())

crime_freq_by_month

Visualize Data: Crime by Month and Type

graph <- ggplot(crime_freq_by_month, aes(x = Month, y = frequency)) + 
  geom_line(aes(group=Crime.type, color = Crime.type)) +
  #geom_point(size = 2, shape = 21, fill = "lightblue") +
  theme(legend.position = "top") +
  labs(x="Month", y="Number of Crimes") + 
  ggtitle("Monthly Crime Frequency in the UK") +
  theme_hc() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggplotly(graph, tooltip = c("x", "y", "group"), dynamicTicks=TRUE)

Insights

ASB is the most frequent crime, followed by violent and sexual offenses

seasonal crime peaks: - anti-social behavior(ASB) summer - violent and sexual offences summer - other theft summer - bicycle theft summer - public order summer - burglary fall to early winter - theft from the person early winter (December)

the seasonality of crime is most notable for: - ASB (approx 45% more crime during peak season) - bike theft (approx 100% more crime during peak season)

Note: Increase in crime is not a reflection of increase in population, which was only about 2% from 2014 to 2017 (office for national statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/overviewoftheukpopulation/august2019)